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MTAF: AN ADAPTIVE DESIGN FOR KEYWORD BASED CONTENT DISSEMINATION ON DHT NETWORKS

ABSTRACT:

In this paper, the study about beyond offering the widely used keyword search function. Many peer-to-peer systems nowadays support the subscription function. The key of MTAF is to carefully select a subset of terms without incurring false negatives and to forward the content item toward the home nodes of such selected terms for low content forwarding cost. In particular, the similarity-based replication of filters is shown to mitigate the effect of hot spots that arise due to the fact that some document terms are substantially more popular than the others. For example, Vuze allows users to create subscription filters based on the keyword search. Given the subscription, episodic or related content will be delivered to the users whenever new episodes are available. Unfortunately, these applications suffer from the downsides, for example, high network traffic in the nodes maintaining popular terms.

EXISTING SYSTEM	PROPOSED SYSTEM
EXISTING CONCEPT:- Existing system in keyword search, many P2P systems nowadays support the subscription function when many users subscribe to the search filters containing a very popular keyword, the peer nodes maintaining such a keyword suffer from high network traffic caused by the periodical running scheme.   Yet a too long period cannot offer the timely alert in case those users have the requirement to find the most recent results.	PROPOSED CONCEPT:- Proposed system in match the content with queries (and filters) based on keywords. Main element of these architectures is that the implementation utilizes the key-to-node mapping of the DHT to designate one of the peer nodes as a home node for each content term.   Forwarding an item of the metadata content is proportional to the number of distinct terms to reduce the high content forwarding cost. .
EXISTING ALGORITHM:- Centralized in Preliminary Algorithm.	PROPOSED ALGORITHM:- Greedy Algorithm.
ALGORITHM DEFNITION:-   We proposed a centralized algorithm. Our DHT solution uses the key element of adaptively replicating filters based on the analytical cost model that we have built.	ALGORITHM DEFNITION:-   Algorithm of the set cover problem to select a subset of terms, the physical node then retrieves the associated positing lists and matches with the filters.
DRAWBACKS:- Incurring high Retrieval and local filters overhead.   It does reduce the number of selected terms sufficiently for practical applications.	ADVANTAGES:- Very short filter lengths in real datasets.     It does not reduce the number of selected terms sufficiently for practical applications.